1. Technical Field
The present disclosure is directed to systems and methods for monitoring, diagnosis and condition-based maintenance of mechanical systems, and more particularly to systems and methods that employ intelligent model-based diagnostic methodologies to effectuate such monitoring, diagnosis and maintenance. According to exemplary embodiments of the present disclosure, the intelligent model-based diagnostic methodologies advantageously combine or integrate quantitative (analytical) models and graph-based dependency models to enhance diagnostic performance.
2. Background of the Disclosure
Recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of vehicles is monitored and managed. These advances facilitate remote monitoring, diagnosis and condition-based maintenance of automotive systems. With the increased sophistication of electronic control systems in vehicles, there is a concomitant increased difficulty in the identification of malfunction phenomena. Consequently, current rule-based diagnostic systems are difficult to develop, validate and maintain.
The increasing complexity of modern computer-controlled systems, ranging from automobiles, aircraft, power, manufacturing, chemical processes, transportation, and industrial machines/equipment, has made system monitoring an inevitable component of system operations. For example, with the increased sophistication of electronic control systems in vehicles, there is a concomitant increased difficulty in the identification of the malfunction phenomena (subsystem failure modes, ambiguity caused by cross-subsystem failure propagation) [See, P. Struss et al., “Model-based tools for integration of design and diagnosis into a common process—a project report,” 13th International Workshop on Principles of Diagnosis (DX02), Semmering, Austria, 2002.] Consequently, current rule-based monitoring systems are difficult to develop, validate and maintain.
Recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of systems is monitored and managed. The availability of data (sensor, command, activity and error code logs) collected during nominal and faulty conditions, coupled with intelligent health management techniques, can help to ensure continuous system operation by recognizing anomalies in system behavior, isolating their root causes, and assisting system operators and maintenance personnel in executing appropriate remedial actions to remove the effects of abnormal behavior from the system. A continuous monitoring and early warning capability that relates detected degradations in systems to accurate remaining life-time predictions are essential to economical operation of systems. Such a capability will minimize downtime, improve resource management via condition-based maintenance, and minimize operational costs.
Automotive engineers have found quantitative simulation to be a vital tool for a variety of functions, including algorithm design for electrical control unit (ECU), rapid prototyping, programming a hardware-in-the-loop (HIL) test system, production code generation and process management documentation. Applications of this model-based design approach include design efforts involving engine control and automatic transmission systems. With the availability of mathematical models and computer-aided engineering information, it is natural to integrate intelligent model-based diagnostic processes into the initial design phase for vehicle health management. Although the basic research in model-based diagnosis has gained increasing attention for over three decades, with different types of approaches developed for this purpose, there has been little attention directed to integrating disparate diagnostic modeling techniques, especially those that combine quantitative and graph-based dependency models, for intelligent diagnosis. Publications addressing model-based diagnostic approaches and techniques include the following:                Silvio Simani et al., Model-based fault diagnosis in dynamic systems using identification techniques, Springer Verlag publishers, 2003.        Ron J. Patton et al., Issues of fault diagnosis for dynamic systems, Springer Verlag publishers, 2000.        Isermann, R., “Process fault detection based on modeling and estimation methods: a survey,” Automatica, Vol. 20, pp. 387-404, 1984.        Isermann, R., “Fault diagnosis of machines via parameter estimation and knowledge processing-tutorial paper,” Automatica, Vol. 29, No. 4, pp. 815-835, 1993.        Isermann, R., “Supervision, fault-detection and fault-diagnosis methods—an introduction,” Control Eng. Practice, Vol. 5, No. 5, pp. 639-652, 1997.        Paul M. Frank, “Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—a survey and some new result,” Automatica, Vol. 26, No. 3, pp. 459-474, 1990.        
Despite efforts to date, a need remains for improved/enhanced systems and methods for monitoring, diagnosis and/or maintenance of a variety of systems, including, e.g., automobiles, aircraft, power systems, manufacturing systems, chemical processes and systems, transportation systems, and industrial machines/equipment. In order to facilitate continuous system operation, e.g., by recognizing anomalies in system behavior, isolating their root causes, and assisting system operators and maintenance personnel in executing appropriate remedial actions to remove the effects of abnormal behavior from the system, new intelligent model-based diagnostic methodologies that exploit the advances in sensor, telecommunications, computing and software technologies are needed.